Alcohol use disorders (AUDs), consisting of alcohol abuse and alcohol dependence, are a major cause of substantial morbidity and mortality, including frequent association with depressive episodes, severe anxiety, insomnia, suicide, and abuse of other drugs. In the US alone, about 18 million people have an AUD, with an estimated yearly cost of nearly $200 billion. Despite the deleterious impacts of AUDs, few truly effective strategies have been developed to prevent or treat AUDs due to the lack of mechanistic understanding of the AUDs pathogenesis. Numerous brain imaging studies have consistently revealed the impact of AUDs on neurobiological phenotypes (i.e. brain structural and functional neurophentypes such as gray and white matter reduction, or malfunctioning in the mesocorticolimbic system). Biological factors, social and interpersonal influences are considered the major risk factors for the development of AUDs. Although genetic variations are known to be an important contributor to the risk of AUDs, the major genes that would explain a significant portion of the variations in AUD risk have not been identified yet. This is most likely due to the lack of understanding the epigenetic mechanisms involved in gene-environment interactions and the fact that in most of the past studies highly heterogeneous clinical AUD categories were used to study the link or association between AUDs and genetic factors or epigenetic modifications. The goal of this project is to assess the link or association between epigenetic regulation and specific brain neurophenotypes using the brain image data and genetic materials available from the NIH supported Nathan Kline Institute-Rockland Sample. For epigenetic modifications, DNA methylation is the most easily measured type of epigenetic regulation and thus promoter DNA methylation will be used in this study. Aim 1: Examine differences in genome-wide promoter DNA methylation profiles between AUDs and healthy controls across the life span group-matched for age, sex, ethnic/racial background and other confounding factors. The resulting differentially methylated CpG sites in AUDs will also be subjected to bioinformatic and systems analysis to provide further biological insights into the pathogenesis of AUDs. Aim 2: Examine the relationships among promoter DNA methylation patterns and brain neuroophenotypes related to AUDs. Specifically, an integrated multimodal imaging approach will be used to analyze MRI, DTI and fMRI images. Neurophenotypes related to AUDs will provide the brain regions in which we will interrogate brain expression databases to identify differentially expressed genes. We will then seek the DNA methylation profiles related to the differentially expressed genes and identify those that vary systematically across individuals in the brain imaging datasets available to us. In this way, we anticipate we will be able to discover DNA methylation profiles associated with AUDs and with specific neurophenotypes by querying integrated multimodal data including MRI, DTI and fMRI images.